供应商产能预订引擎 — 旺季弹性产能锁定与长期产能保障协议管理

"""
供应商产能预订引擎
功能：产能评估 / 预订决策 / 期权费vs保障价值 / 弹性预订策略
"""
import numpy as np
from dataclasses import dataclass, field
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')


@dataclass
class SupplierCapacity:
    supplier_id: str
    max_monthly_capacity: int
    current_booked_pct: float    # 已被预订比例
    available_capacity: int
    lead_time_weeks: int
    min_booking_qty: int
    hard_booking_deposit_pct: float = 0.15
    soft_option_fee_pct: float = 0.03
    option_validity_weeks: int = 8


@dataclass
class DemandScenario:
    scenario: str
    probability: float
    demand_qty: int
    selling_price: float
    cogs: float


def compute_booking_decision(supplier: SupplierCapacity,
                               scenarios: list,
                               sku_unit_cost: float) -> dict:
    """计算最优产能预订量和策略"""

    # 情景分析：不同需求量下的期望利润
    booking_options = range(supplier.min_booking_qty,
                             min(supplier.available_capacity, 20001), 1000)
    best_qty = supplier.min_booking_qty
    best_ev = -float('inf')

    for book_qty in booking_options:
        ev = 0.0
        for sc in scenarios:
            actual_sales = min(book_qty, sc.demand_qty)
            revenue = actual_sales * sc.selling_price
            prod_cost = book_qty * sc.cogs          # 生产了才付成本
            unsold_cost = max(0, book_qty - sc.demand_qty) * sc.cogs * 0.3  # 滞销30%损失
            profit = revenue - prod_cost - unsold_cost
            ev += sc.probability * profit

        # 减去预订成本
        booking_cost = book_qty * sku_unit_cost * supplier.hard_booking_deposit_pct
        net_ev = ev - booking_cost

        if net_ev > best_ev:
            best_ev = net_ev